分支局部分流何时起作用：树突状兴奋/抑制网络的增益-负载对齐原理
When Branch-Local Shunting Helps: A Gain-Load-Alignment Principle for Dendritic E/I Networks
AI总结：
研究生物神经元树突状兴奋/抑制网络中分流整合，引入可训练框架DendriNet，通过改变多种因素研究其性能，发现遵循增益-负载对齐原理，在不同情况下深度分流与其他方法各有优劣，形态影响信号位置，深度和分流本身无绝对优势。
AI中文摘要：
生物神经元通过分流在分支树突上结合兴奋和抑制活动，其中抑制以除法方式减弱兴奋。尚不清楚这是否比相同非负输入的加法兴奋/抑制整合能改善群体读出。我们引入了DendriNet，一个可训练框架，它能改变整合规则、形态、突触分配、除数局部性和树突非线性。对于具有乘法增益的群体编码，任何可实现的分流读出的局部线性化都会在正加法兴奋/抑制锥内产生一个决策方向；匹配加法最优需要一个正的自洽分流实现。每个标量分流阈值也都有一个精确的仿射加法实现。超出这个局部极限，性能遵循增益-负载对齐原理：当可靠的除数抑制信号对齐增益的程度超过它减弱信号或增加分母变异性时，分支局部分流会有帮助。被动加法树会扁平化到线性读出，而分流树会组合局部除数。在设计的层次结构中，深度分流优于切线和拟合线性控制，但灵活的非线性预测器在有足够标签时会超过它。支持洗牌会颠倒线性比较结果，传感器损坏会颠倒拟合线性比较结果，资源匹配的激活训练没有显示出一致的深度优势。相同的支持和可靠性相互作用也出现在冻结特征归一化中。在三个小鼠V1会话中，分流超过加法解码器的差距在窄读出时最大，在最宽读出时在强私有噪声下会反转，并且在不同运行状态下会变化。形态可以确定可靠的干扰估计与任务相关信号相遇的位置，但深度和分流本身都没有优势。
英文摘要：
Biological neurons combine excitatory and inhibitory (E/I) activity on branched dendrites through shunting, in which inhibition divisively attenuates excitation. Whether this improves population readout over additive E/I integration of the same nonnegative inputs remains unclear. We introduce DendriNet, a trainable framework that varies integration rule, morphology, synaptic allocation, divisor locality, and dendritic nonlinearities. For population codes with multiplicative gain, a local linearization of any realizable shunting readout yields a decision direction within the positive additive E/I cone; matching the additive optimum requires a positive self-consistent shunting realization. Every scalar shunting threshold also has an exact affine additive realization. Beyond this local limit, performance follows a gain-load-alignment principle: branch-local shunting helps when a reliable divisor suppresses signal-aligned gain more than it attenuates signal or adds denominator variability. Passive additive trees flatten to linear readouts, whereas shunting trees compose local divisors. In a designed hierarchy, deep shunting outperforms tangent and fitted-linear controls, but flexible nonlinear predictors overtake it with enough labels. Support shuffling reverses the linear comparisons, sensor corruption reverses the fitted-linear comparison, and resource-matched activated training shows no consistent depth benefit. The same support and reliability interaction appears in frozen-feature normalization. Across three mouse V1 sessions, the shunting-over-additive decoder gap is largest for narrow readouts, reverses under strong private noise at the widest readout, and varies across running states. Morphology can determine where reliable nuisance estimates meet task-relevant signals, but neither depth nor shunting is intrinsically advantageous.
